In cancer, genetic heterogeneity is the focus of many investigations as it plays important roles in tumor progression and drug resistance by driving phenotypic diversity. Here, we consider another type of heterogeneity, one where natural cell-to-cell variability in protein levels in genetically-identical mammalian cells causes the same stimuli to yield different cell fates, such as life or death. We term this phenomenon """"""""natural phenotypic divergence"""""""" (NPD). NPD can manifest as, for example, a persistent anticancer drug resistant subpopulation of cells, and understanding it is important for predicting cancer treatment efficacy. However, means to predict NPD from cell-based experiments have not been developed and are the subject of the proposal. We hypothesize that NPD can be predicted by characterizing how multivariate, endogenous protein expression noise is propagated non-linearly through signaling networks to regulate cell fate. It is the endogenous expression and degradation noise in the levels of multiple proteins within a signaling network that collectively manifest as NPD. We will test this hypothesis by combining experimental and computational approaches to examine NPD-based proliferation of non-transformed MCF10A cells. This proliferation is induced by combinations of epidermal growth factor, insulin, and cortisol and mediated by activation of the ERK, Akt, JNK, and SGK pathways. First, experimentally, we will use live-cell imaging approaches with FRET probes to measure real-time signaling network dynamics and proliferation simultaneously. Although we can only measure one pathway at a time, our subsequent use of computational, dynamic modular response analysis theory allows us to reconstruct how these pathways dynamically interact in a stimulus-specific fashion to control stochastic proliferation fates. Second, we will build a chemical kinetics-based, stochastic computational model that simulates how the protein expression variability underlying NPD propagates into signaling dynamics heterogeneity. Analysis of this model will suggest sets of key proteins whose collective, multivariate fluctuations have a large influence on NPD-based proliferation. Finally, we will measure fluctuations in the levels of these key proteins in single live cells, use our computational models to predict whether these cells should proliferate or not in response to defined perturbations, and test the predictions by observing the actual proliferation decision in those same cells. We will test such predictions not only in standard 2D cell culture models, but also in the context of 3D culture acini formation. If successful, this would be the first demonstration that the stochastic fates of individual live cells could be predicted based on biomarkers present prior to perturbation. This would be an important step towards identifying biomarker sets for individual patients and fashioning personalized therapeutic strategies.
Cancer cells within a tumor are typically very different from one another, and we cannot generally predict whether individual cells will be sensitive or resistant to treatment. This proposal entails a combination of experimental and mathematical modeling methods to understand how one might predict whether individual cells will proliferate or not in response to a treatment. The ability to predict stochastic fates of individual cells in response to treatment can help catalyze the improvement of current and the proposal of new cancer treatment strategies.
|Farrelly, L A; Dill, B D; Molina, H et al. (2016) Current Proteomic Methods to Investigate the Dynamics of Histone Turnover in the Central Nervous System. Methods Enzymol 574:331-54|
|Stern, Alan D; Rahman, Adeeb H; Birtwistle, Marc R (2016) Cell size assays for mass cytometry. Cytometry A :|
|Smith, Gregory R; Birtwistle, Marc R (2016) A Mechanistic Beta-Binomial Probability Model for mRNA Sequencing Data. PLoS One 11:e0157828|
|Byrne, Kate M; Monsefi, Naser; Dawson, John C et al. (2016) Bistability in the Rac1, PAK, and RhoA Signaling Network Drives Actin Cytoskeleton Dynamics and Cell Motility Switches. Cell Syst 2:38-48|
|Milewska, Malgorzata; Romano, David; Herrero, Ana et al. (2015) Mitogen-Inducible Gene-6 Mediates Feedback Inhibition from Mutated BRAF towards the Epidermal Growth Factor Receptor and Thereby Limits Malignant Transformation. PLoS One 10:e0129859|
|Maze, Ian; Wenderski, Wendy; Noh, Kyung-Min et al. (2015) Critical Role of Histone Turnover in Neuronal Transcription and Plasticity. Neuron 87:77-94|
|Birtwistle, Marc R (2015) Analytical reduction of combinatorial complexity arising from multiple protein modification sites. J R Soc Interface 12:|
|Levin, Victor A; Tonge, Peter J; Gallo, James M et al. (2015) CNS Anticancer Drug Discovery and Development Conference White Paper. Neuro Oncol 17 Suppl 6:vi1-26|
|Gallo, James M; Birtwistle, Marc R (2015) Network pharmacodynamic models for customized cancer therapy. Wiley Interdiscip Rev Syst Biol Med 7:243-51|
|Klinke 2nd, David J; Birtwistle, Marc R (2015) In silico model-based inference: an emerging approach for inverse problems in engineering better medicines. Curr Opin Chem Eng 10:14-24|
Showing the most recent 10 out of 12 publications